Diakite et al. BMC Medical Genetics(2020) 21:206
https://doi.org/10.1186/s12881-020-01133-8

RESEARCH ARTICLE

Open Accessp.Arg72Pro polymorphism of P53 andbreast cancer risk: a meta-analysis of casecontrol studies
Brehima Diakite1,2,3* , Yaya Kassogue1,2,3, Guimogo Dolo1,2, Jun Wang3,4, Erin Neuschler5, Oumar Kassogue1,2,
Mamadou L Keita2, Cheick B Traore1,2, Bakarou Kamate1,2, Etienne Dembele3,4, Sellama Nadifi6, Robert L Murphy3,4,
Seydou Doumbia1,2, Lifang Hou3,4† and Mamoudou Maiga1,3,4†Abstract
Background: The effect of the p.Arg72Pro variant of the P53 gene on the risk of development ofbreast cancer
remains variable in populations. However, the use ofstrategies such aspoolingage-matched controls with disease
may provide a consistent meta-analysis. Our goal was to perform a meta-analysis in order to assess the associationof p.Arg72Pro variant of P53 gene with the risk of breast cancer.
Methods: Databases such as PubMed, Genetics Medical Literature, Harvard University Library, Web of Science andGenesis Library were used to search articles. Case-control studies with age-matched on breast cancer
havingevaluated the genotype frequencies of the TP53 p.Arg72Pro polymorphism were selected. The fixed andrandom effects (Mantel-Haenszel) were calculated using pooled odds ratio of 95% CI to determine the risk of
disease. Inconsistency was calculated to determine heterogeneity among the studies. The publication bias wasestimated using the funnel plot.
Results: Twenty-one publications with 7841 cases and 8876 controls were evaluated in this meta-analysis. Overall,our results suggested that TP53 p.Arg72Pro was associated with the risk of breast cancer for the dominant model(OR = 1.09, 95% CI = 1.02–1.16, P = 0.01) and the additive model (OR = 1.09, 95% CI = 1.01–1.17, P = 0.03), but not for
the recessive model (OR = 1.07, 95% CI = 0.97–1.18, P = 0.19). According to the ethnic group analysis, Pro allele wasassociated with the risk of breast cancer in Caucasians for the dominant model and additive model (P = 0.02), andAfricans for the recessive model and additive model (P = 0.03).
Conclusions: This meta-analysis found a significant association between TP53 p.Arg72Pro polymorphism and therisk of breast cancer. Individuals carrying at least one Pro allele were more likely to have breast cancer than
individuals harboring the Arg allele.
Keywords: P53 gene, p.Arg/pro polymorphism, Breast cancer, Meta-analysis* Correspondence: br.diakite@yahoo.fr†
Lifang Hou and MamoudouMaiga are Last author.
1
Faculty of Medicine and Odontostomatology, 1805, Université des Sciences,
des Techniques et des Technologies Sciences de Bamako (USTTB),
Hamdallaye ACI, 2000 Bamako, Mali
2
Teaching Hospital Center of Point G, 333 Bamako, Mali
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Diakite et al. BMC Medical Genetics(2020) 21:206

Background
Breast cancer is a multifactorial disease which constitutes a major public health problem [1]. In 2018, theWorld Health Organization reported that 2.09 million
new cases of breast cancer were detected [1] comparedto 1.38 million cases in 2008 [2]. It is the leading causeof death in women around the world. It should be notedthat the incidence of breast cancer differs among different populations around the world [1]. Over the past decades, major advances have been made in understanding
the pathology of breast cancer at the molecular level, including the involvement of certain genes associated withthe development of the disease such as BRCA1, BRCA2
and P53 which produce tumor suppressor proteins andparticipate in damaged DNA repair [3–5]. P53 plays a
key role in the regulation of cell proliferation and apoptosis. The P53 protein is essential for maintaining the integrity of the cell and its components. In humancancers, mutated P53 produces abnormal proteins thatalter or inhibit transcriptional regulation [6]. As a result,the stress response, cell cycle as well as apoptosis are affected. Inactivation or mutation of P53 gene would leadin linkage disequilibrium in the DNA sequence, which,associated with chromosomal aberrations induce the appearance of genomic instability and later the development of cancer [7, 8]. The molecular signature of humancancers shows that this gene is frequently observed in its
mutated form [9]. P53 has been mapped on chromosome 17p13 and contains 11 exons. Several single nucleotide polymorphisms (SNP) have been identified andthe most studied variant is the substitution of Arginine
by Proline at position 72 in exon 4. Studies carried out
on different populations around the world have shownthat this SNP is associated with the development of numerous diseases including cancers [10, 11]. It should be
noted that, many association studies have examined therelationship between the SNP p.Arg72Pro of P53 geneand the risk of breast cancer, however, the reports fromthese studies remain conflicting as some studies haveshown that p.Arg72Pro is associated with the risk ofbreast cancer, while others found no association. Menzel
et al. 2004 [12] and Akkiprik et al. 2009 [13] in their investigations showed a link between p.Arg72Pro and therisk of breast cancer. However, other authors who carried out a case-control study in which participants ages
were not matched in a similar population, have concluded that p.Arg72Pro was not associated with the riskof breast cancer [14]. These different results with diverging conclusions can be explained by a very strong heterogeneity in allele and genotype distribution ofp.Arg72Pro of the P53 gene. This heterogeneity may berelated not only to the geographic and ethnic origin[15–17] but also to the study design such as non-agematched case-control studies. Based on these above

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observations, we hypothesized that the p.Arg72Pro polymorphism of P53 gene may represent a potentially important genetic marker, contributing to breast cancer
susceptibility in Caucasian, Asians and Africans. The
present meta-analysis included only age-matched casecontrol studies in order to statistically decrease the heterogeneity between the studies, to qualitatively assess
the effect of p.Arg72Pro on the risk of breast cancer. We
have performed an independent two-stage meta-analysis;
overall and sub-group analysis.

Methods
Literature search

The Pubmed Genetics Medical Literature Database, theHarvard University Library, and the Web of Science andGenesis Library were used to identify available articles
published in English. The keywords “P53”, “p.Arg72Pro”
and “polymorphism” or “mutation” or “gene” and “breast
cancer”cited in the genetic association studies were used
to detect and select scientific manuscripts in these databases. We also reviewed references cited in these studies
to identify additional articles that were not identified byour research in the databases.
Inclusion criteria

The inclusion criteria included: (1) published casecontrol studies as an original article to evaluate the association between p.Arg72Pro of the P53 gene and the riskof breast cancer, (2) full manuscript available, (3) casecontrol study with age-matched, (4) distribution of genotype respecting Hardy-Weinberg equilibrium (HWE) incontrols, (5) availability of the three genotypic frequencies (Arg/Arg, Arg/Pro and Pro/Pro) in the case and control groups. (6) Study no influencing the pooled odd
ratio (OR) values. Three investigators independently
evaluated each study to determine eligibility.
Data extraction

The data were collected by an investigator and verified
by a second investigator to reach consensus on all
points. First author, year of publication, country, ethnicity of study population, sample size, age-matched, distribution of genotype andalleles, as well as therecalculation of HWE in controls were extracted fromthe eligible studies. A third reviewer made a contradictory assessment to reconcile the assumptions. The dataof controls evaluated with p.Arg72Pro variant were included in this meta-analysis.
Statistical analysis

Chi2 analysis with a significance level of P < 0.05 wasused to evaluate whether p.Arg72Pro polymorphism distribution of the P53 gene in controls fits HWE. The association between the p.Arg72Pro and the risk of breast

Diakite et al. BMC Medical Genetics(2020) 21:206

cancer was evaluated by the Odd ratio (OR) of 95% CI.
We evaluated the strength of association between thep.Arg72Pro polymorphism of P53 gene and the risk ofbreast cancer using different genetic models, including
the dominant (Pro/Pro + Arg/Pro vs. Arg/Arg), recessive(Pro/Pro vs. Arg/Arg + Arg/Pro) and the additive (Pro vs.
Arg). Heterogeneity among the studies was assessed by
I2 statistical test [18, 19]. If I2 > 50% (presence of heterogeneity), the random effects model was used to calculate
the overall OR, otherwise in case of lack of heterogeneity, the fixed effects method was used. We also have examined the funnel plot to determine publication bias
[20]. All statistical analyses were performed with Review
Manager Software version 5.1.

Results
Characteristic of eligible studies

Figure 1 summarizes the process of selecting studies thatthe inclusion criteria. In sum, 21 eligible age-matchedcase-control studies were selected for the pooled OR
analyses. Genotype distribution of the control populationthat met HWE was a minimum requirement for studies
to be retained for the meta-analysis. Out of the 21 studies (7841cases and8876 controls), eleven were Caucasians [12–14, 23–28, 36, 37], nine were Asians [21, 29–
35, 38] and one was African [22] (Table 1).

Fig. 1 Flow diagram of the studies evaluated for meta-analysis

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Quantitative analysis

Table 2 shows pooled ORs and heterogeneity testresultsof the association between the TP53 p.Arg72Pro polymorphism and the risk of breast cancer. Overall, aslightly association of TP53 p.Arg72Pro polymorphismwith the risk of breast cancer was observed for the dominant (OR = 1.09, 95% CI = 1.02–1.16, P = 0.01, Fig. 2)
and additive (OR = 1.09, 95% CI = 1.01–1.17, P = 0.03,
Fig. 3) models, but not for the recessive model (OR =1.07, 95% CI = 0.97–1.18, P = 0.19, Fig. 4). In the subgroup analyzes, except the recessive model (OR = 1.18,
95% CI = 0.96–1.44; P = 0.12), we noted a moderate association of p. Arg72Pro with the risk of breast cancer
for the dominant (OR = 1.09, 95% CI = 1.01–1.17, P =0.02) and additive (OR = 1.07, 95% CI = 1.01–1.14, P =0.02) models in Caucasians (Fig. 5). When considering
the Asian population, the different genetic models
showed no trend (recessive: OR = 1.01, 95% CI = 0.87–
1.17, P = 0.88; dominant: OR = 1.06, 95% CI = 0.94–1.20;
P = 0.33; additive; OR = 1.06, 95% CI = 0.91–1.23, P =0.46) (Fig. 6). The only eligible African study showedthat the TP53 p.Arg72Pro polymorphism is highly associated with the risk of breast cancer as well in the recessive model (OR = 2.14, 95% CI = 1.08–4.23, P = 0.03)
than in the additive model (OR = 1.49, 95% CI = 1.03–
2.16, P = 0.03).

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Diakite et al. BMC Medical Genetics

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Table 1 Genotypes distribution of TP53 p.Arg72Pro in breast cancer cases and controls
Cases

Controls

Authors

Ethnicity

N

Arg/Arg

Arg/ProPro/ProN

Arg/Arg

Arg/ProPro/Pro

HWE

Akkiprik et al. 2009 [13]Caucasian

95

25

50

20

107

46

49

12

Yes

Alshatwi et al. 2012 [21]

Asian

100

22

52

26

100

32

51

17

Yes

Ayoubi et al. 2018 [22]African

125

55

42

28

126

65

46

15

Yes

Buyru et al. 2003 [23]Caucasian

115

64

39

12

63

26

28

9

Yes

Cherdyntseva et al. 2012 [24]Caucasian

388

184

162

42

275

148

100

27

Yes

Costa et al. 2008 [25]Caucasian

175

98

61

16

212

124

70

18

Yes

Cox et al. 2007 [26]Caucasian

1477

804

569

104

2224

1255

838

131

Yes

Denisov et al. 2009 [27]Caucasian

297

148

124

25

275

147

99

29

Yes

Ebner et al. 2010 [28]Caucasian

263

138

108

17

254

137

103

14

Yes

Hossain et al. 2016 [29]

Asian

125

54

42

29

125

61

51

13

Yes

Isakova et al. 2017 [30]

Asian

117

57

50

10

102

53

36

13

Yes

Katiyar et al. 2003 [31]

Asian

77

20

51

6

41

9

24

8

Yes

krivokuca et al. 2014 [14]Caucasian

155

87

58

10

114

62

45

7

Yes

Li et al. 2002 [32]

Asian

28

11

10

7

50

10

26

14

Yes

Ma et al. 2006 [33]

Asian

404

149

178

77

472

150

222

100

Yes

Menzel et al. 2004 [12]Caucasian

302

158

114

30

475

275

170

30

Yes

Sharma et al. 2014 [34]

Asian

200

47

103

50

200

67

91

42

Yes

Song et al. 2009 [35]

Asian

1110

341

547

222

1097

355

514

228

Yes

Sprague et al. 2007 [36]Caucasian

1653

909

644

100

1854

1021

704

129

yes

Wang-Gohrke et al. 2002 [37]

Caucasian

552

282

221

49

543

300

203

40

yes

Zhang et al. 2007 [38]

Asian

83

21

45

17

167

47

87

33

yes

N Number, HWE, Hardy-Weinberg Equilibrium

Table 2 Distribution of TP53 p.Arg72Pro polymorphism according to the different genetic models
Sample size

Genetic

PooledGroup

N

Cases/Controls

ModelsOR (95% CI)

P-value

I2

PhetOverall

21

7841/8876
Dominant

1.09 (1.02–1.16) FE

0.01

31%

0.09

Recessive

1.07 (0.97–1.18) FE

0.19

36%

0.05

Additive1.09 (1.01–1.17) FE

0.03

47%

0.01

Dominant

1.09 (1.01–1.17) FE

0.02

27%

0.19

Recessive

1.09 (0.95–1.25) FE

0.22

10%

0.34

Additive1.07 (1.01–1.14) FE

0.02

39%

0.09

Dominant

1.06 (0.94–1.20) FE

0.33

45%

0.07

Recessive

1.01 (0.87–1.17) FE

0.88

48%

0.05

Additive1.06 (0.91–1.23) RE

0.46

54%

0.03

Dominant

1.36 (0.83–2.23)

0.23

Recessive

2.14 (1.08–4.23)

0.03

–

–

Additive

1.49 (1.03–2.16)

0.03

–

–

Caucasian

Asian

African1191

Heterogeneity5472/6396

2244/2354

125/126*: Significant, P: p value OR; I2: Inconsistency; dominant model: Pro/Pro + Arg/Pro vs. Arg/Arg; recessive model: Pro/Pro vs. Arg/Arg + Arg/Pro; additive model: Pro vs.
Arg; Phet: P value of Heterogeneity, FE Fixed effect, RE Random effect, N Number

Diakite et al. BMC Medical Genetics(2020) 21:206

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Fig. 2 Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the dominant model. The black diamonddenotes the pooled OR; blue squares indicate the OR in each study with square sizes inversely proportional to the standard error of the OR; andhorizontal lines represent the 95% CI

Fig. 3 Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the recessive model. The black diamonddenotes the pooled OR; blue squares indicate the OR in each study with square sizes inversely proportional to the standard error of the OR; andhorizontal lines represent the 95% CI

Diakite et al. BMC Medical Genetics(2020) 21:206

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Fig. 4 Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the additive model. The black diamonddenotes the pooled OR; blue squares indicate the OR in each study with square sizes inversely proportional to the standard error of the OR; andhorizontal lines represent the 95% CI

Sensitive analysis

To maintain the stability of the meta-analysis after the
non-inclusion of deviant studies of HWE, we evaluatedthe influence of each study on pooled OR. After the exclusion of studies [39–42],no study has shown a significant influence of pooled OR effect and p-values for thedifferent genetic models (Table 2).
Sources of heterogeneity

To avoid large heterogeneity, we excluded studies inwhich the distribution of genotypes deviated from the
HWE equilibrium. The sensitivity analysis overall,showed moderate heterogeneity (I2 < 50%) in the recessive and dominant models. We noted the same tendencyof heterogeneity when considering all the data (I2 = 47%,
P = 0.01) and among Asians (I2 = 54%, P = 0.03) for the
additive model (Table 2). In addition, we compared thepooled OR of the fixed and random effects, no statistically significant difference was found between the two
effects, which supports strongly the consistency of thepresent study’s data.
Publication Bias

The publication bias was assessed using the funnel plot.
After the exclusion of studies deviating from HWE andthose influencing the pooled ORs values, no significant
publication bias was found in the different genetic
models (Fig. 7).

Discussion
Like other multifactorial diseases, the causes of breast
cancer are not known. However, several factors combine
their effects for the development of the disease. These
factors are of clinical, biological, environmental and genetic origin [43, 44]. From a genomic point of view, it has
been reported that genetic polymorphisms of the p53
gene can influence the development of cancers [45].
However, the mechanism by which these polymorphisms
affect cancer development remain unknown. Functionally, these polymorphisms alter alternative splicing andthus affecting mRNA stability and protein synthesis. The
normal P53 gene produces a protein that plays a key role
in DNA repair, cell cycle control and apoptosis [45].
Through this physiological role P53 acts as a guardian of
the genome, preventing the malignant transformation of
normal cells. In the event of a mutation, the function of
p53 is impaired, leading to the appearance of malignant
cells and later cancerous disease [46–48]. It has been reported that the R72 variant of the P53 mutant inaddition to influencing the onset of cancer is also associated with a bad prognosis through the rapid onset ofmetastasis [49].
In the present meta-analysis, we examined the relationship between TP53 p.Arg72Pro polymorphism andthe risk of breast cancer. Overall, our findings showedthat the dominant and additive models were associatedwith an increased risk of breast cancer for the carriers ofDiakite et al. BMC Medical Genetics(2020) 21:206

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Fig. 5 Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the a dominant model, b recessive modeland c additive model in Caucasians. The black diamond denotes the pooled OR; blue squares indicate the OR in each study with square sizes
inversely proportional to the standard error of the OR; and horizontal lines represent the 95% CI

72Pro allele. These results corroborate with the data reported from two recent meta-analyzes, the first covering
eleven studies with 950 cases and 882 controls in the
Asian population [50] and the second performed on the
Indian population which covered seven studies with
1249 cases and 1838 controls [51]. These authors intheir analysis showed that the dominant and the additivemodels were associated with the risk of breast cancer.
Contrary to our results, other meta-analyzes found conflicting results [52, 53]. The works of Zhuo et al. 2009,
Francisco et al. 2011, Ma et al. 2011 and Concalves et al.
2014 also reported a decreased risk of breast cancer with

Diakite et al. BMC Medical Genetics(2020) 21:206

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Fig. 6 Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the a dominant model, b recessive modeland c additive model in Asians. The black diamond denotes the pooled OR; blue squares indicate the OR in each study with square sizes
inversely proportional to the standard error of the OR; and horizontal lines represent the 95% CI

the different genetic models applied [54–57]. These differences might be explained by the samples size, types ofallelic variant and eligible studies included. In the subgroup analysis, our meta-analysis revealed a high risk ofbreast cancer with TP53 p.Arg72Pro in Caucasians(dominant model and additive model) and Africans (recessive and additive models). These trends were consistent with previous studies [12, 13, 22] but inconsistent

with the findings of other studies [52, 53]. However,
Jafrin et al. 2020 concluded that TP53 p.Arg/Pro was associated with the risk of breast cancer in the South
Asian population. The difference between the studies
could be explained by the ethnicity and study design.
The effects of ethnicity may be due to several factors, allelic heterogeneity, gene-gene and gene-environment
interaction and linkage disequilibrium [58–61]. In theDiakite et al. BMC Medical Genetics(2020) 21:206

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Fig. 7 Funnel plots of dominant a, recessive b and additive c model precision by OR

previous meta-analyzes, the selection criteria of studies
were not sufficiently robust such as inclusion of the ageunmatched case-control studies and the inclusion ofstudies with control groups not satisfying HWE [62–75].
The major advantage of the present meta-analysis was
the inclusion of a large number of samples, including
very selective criteria to measure the strength of the association between this polymorphism in exon 4 of TP53
gene and the risk of breast cancer using different genetic
models. However, several limitations need to behighlighted, sample size and small number of casecontrol age-matched studies in ethnic groups.

Conclusion
In the light of this meta-analysis, we noticed that individuals carrying at least one Pro allele of the P53 geneare more likely to have breast cancer with dominant andadditive models than individuals harboring the wild-type
Arg allele. Our study further strengthened and confirmed the hypothesis that the P53 gene is usually mutated in about half of breast cancer cases. For thestability and homogeneity of results from meta-analysis,
future similar studies should take into account selection
criteria for articles such as no deviation from HWE inthe control group and the matching of cases and controls according to age.

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12881-020-01133-8.
Additional file 1. Availability of all data and references with PubMed
accession numbers.
Abbreviations
Arg: Arginine; CI: Confidence interval; Fig.: Figure; HWE: Hady-Weinberg
Equilibrium; I2: Inconsistency; N: Number; OR: Odd ratio; P: P value;
Pro: Proline; SNP: Single nucleotide polymorphisms
Acknowledgments
The authors thank Harvard University, Boston University, Northwestern
University, and University of New Mexico (HBNU) Consortium, Global Health,
Fogarty International Center and the NIH for their training support. Wealso
thankthe Faculty of Medicine and Odontostomatology of the Universitédes
Sciences, Techniques et Technologies de Bamako, the University ClinicalResearch Center (UCRC-Mali), Intelligence Center of Excellence Mali (ICERMali) particularly Prof Cheick Fantamady Traore, Prof. Mamadou Diakite andDr. Mamadou Coulibaly for logistical support.
Authors’ contributions
All authors read and approved the final manuscript. Study concept anddesign: BD, YK, GD, JW, EN, OK, MLK, CBT, BK, ED, SN,SD, LH, MM. Acquisition,
analysis and interpretationof data: BD, YK, GD,MM. Drafting of themanuscript: BD with assistance from by YK, MM. Critical revision of themanuscript for important intellectualcontent: JW, EN, SN, GD, SD. Obtaining
supervision: LH, RLM.
Funding
Research reported in this publication was supported by the HBNU
Consortium, Fogarty International Center and the National Institutes of
Health under Award Number D43 TW010543. The content is solely theDiakite et al. BMC Medical Genetics(2020) 21:206

responsibility of the authors and does not necessarily represent the official
views of the National Institutes of Health.
Availability of data and materials
The dataset analyzed for thisstudyis available from the Additional file 1.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Faculty of Medicine and Odontostomatology, 1805, Université des Sciences,
des Techniques et des Technologies Sciences de Bamako (USTTB),
Hamdallaye ACI, 2000 Bamako, Mali. 2Teaching Hospital Center of Point G,
333 Bamako, Mali. 3Preventive Medicine Department, Cancer Epidemiology
and Prevention, Northwestern University, Chicago, IL 60611, USA. 4Institutefor Global Health, Northwestern University, IL60611, Chicago, USA.
5
Department of Radiology, College of Medicine, University of Illinois at
Chicago, Chicago, IL 60612, USA. 6Hassan II University Aïn chock, 20000
Casablanca,19, Rue Tarik Ibnou Ziad,, Morocco.
Received: 22 November 2019 Accepted: 24 September 2020

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